Client Availability

Client availability, the intermittent participation of devices in distributed machine learning tasks like federated learning, is a critical challenge impacting model accuracy and convergence speed. Research focuses on developing algorithms, such as FedAvg variants and graph-based sampling methods, that mitigate the effects of heterogeneous and correlated client unavailability, often employing techniques like surrogate updates or dynamic client weighting to compensate for missing data. Addressing this challenge is crucial for the successful deployment of federated learning and other distributed systems across diverse and unreliable environments, improving the robustness and fairness of machine learning models in real-world applications.